code: def test_BondEncoder():
      import torch
  
      from deepchem.feat.molecule_featurizers.conformer_featurizer import (
          full_bond_feature_dims,
      )
      from deepchem.models.torch_models.pna_gnn import BondEncoder
  
      bond_encoder = BondEncoder(emb_dim=32)
  
      num_samples = 10
  
      # Create input tensor with values within full_bond_feature_dims
      graph_features = torch.stack(
          [
              torch.randint(low=0, high=dim, size=(num_samples,))
              for dim in full_bond_feature_dims
          ],
          dim=1,
      )
      bond_embeddings = bond_encoder(graph_features)
      assert bond_embeddings.shape == (num_samples, 32)
  
file_path: deepchem/deepchem/models/tests/test_pna.py
imports: import os
  import numpy
  import deepchem
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: test_BondEncoder() without return types